Lyria 3 Pro vs Gemini 3.1 Flash TTS
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Lyria 3 Pro by Google and Gemini 3.1 Flash TTS by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Gemini 3.1 Flash TTS holds a notable pricing advantage for token ingestion. On output generation, Gemini 3.1 Flash TTS is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Lyria 3 Pro provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Gemini 3.1 Flash TTS leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Lyria 3 Pro
フラッグシップ最先端モデルGoogle が提供する Lyria 3 Pro は、最大 131K トークンのコンテキストウィンドウを備え、入力 $1.5/100万トークン・出力 $60/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$1.50 / 1M
$60.00 / 1M
131.1K tokens
N/A
Gemini 3.1 Flash TTS
フラッグシップ最先端モデルGoogle が提供する Gemini 3.1 Flash TTS は、最大 8K トークンのコンテキストウィンドウを備え、入力 $0.75/100万トークン・出力 $12/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.75 / 1M
$12.00 / 1M
8.2K tokens
N/A
ひと目でわかる主な違い
- Gemini 3.1 Flash TTS is 4.7x cheaper for API usage (input + output tokens combined).
- Lyria 3 Pro has a larger context window (131K vs 8K tokens).
- Gemini 3.1 Flash TTS is faster at 120 tok/s vs 50 tok/s.
| Feature | Lyria 3 Pro | Gemini 3.1 Flash TTS |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 80 | 72 |
Provider | ||
Input Price Cost per million input tokens | $1.50 / 1M | $0.75 / 1M |
Output Price Cost per million output tokens | $60.00 / 1M | $12.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $6.30 / 1M | $1.35 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 8.2K tokens~12 A4 pages of text |
Max Output Tokens Per single request | 16K | 4K |
Throughput Speed Tokens generated per second | 50 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
Official evaluation metrics verified by independent third-party research labs.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Lyria 3 Pro · Strengths & Key Advantages
- Google の高度な Full-spectrum neural music and vocal synthesis transformer アーキテクチャと 131K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $1.5/1M、出力 $60/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Gemini 3.1 Flash TTS · Strengths & Key Advantages
- Google の高度な Ultra-low-latency expressive acoustic speech model アーキテクチャと 8K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.75/1M、出力 $12/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Lyria 3 Pro | Gemini 3.1 Flash TTS |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.25 | $0.75 |
Output Tokens / Task Tokens generated per task | 6K | 4K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Lyria 3 Pro | Gemini 3.1 Flash TTS |
|---|---|---|
| Official API Model ID (SDK) | models/lyria-3-pro | models/gemini-3-1-flash-tts |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-03 | 2026-04 |
| Knowledge Cutoff | December 2025 | January 2026 |
| Architecture | Full-spectrum neural music and vocal synthesis transformer | Ultra-low-latency expressive acoustic speech model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.5/1M in) across full 131K window | Flat rate ($0.75/1M in) across full 8K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL | Implicit + Explicit Context Caching at $0/1M with configurable TTL |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Text-only completion (No GUI action loop) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Prompt-based JSON extraction only |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text |
| Output Modalities | audio | audio |
Lyria 3 Pro vs Gemini 3.1 Flash TTS: よくある質問 (FAQ)
Which is more intelligent, Lyria 3 Pro or Gemini 3.1 Flash TTS?
Lyria 3 Pro is more intelligent. It scores 80 compared with 72 on the composite Intelligence Index.
Which model is faster?
Gemini 3.1 Flash TTS is faster, generating 120 tokens per second compared with 50 tokens per second.
Which model is cheaper to run?
Gemini 3.1 Flash TTS is cheaper at $1.35 per 1M tokens blended, compared with $6.30 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.1 Flash TTS has lower latency, with a time to first token of 0.25s compared with 1.50s.
Which model has a larger context window?
Lyria 3 Pro has a larger context window, supporting 131.1K tokens compared with 8.2K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose Lyria 3 Pro if your priority is Soundtrack scoring, gaming background music, and commercial jingle creation. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 3.1 Flash TTS if your workflow prioritizes Real-time voice bots, live telephonic agents, and gaming NPCs. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Lyria 3 Pro compare to Gemini 3.1 Flash TTS in API token pricing and cost per token?+
Lyria 3 Pro costs $1.50 per 1M input tokens and $60.00 per 1M output tokens, whereas Gemini 3.1 Flash TTS costs $0.75 input and $12.00 output. Overall, Gemini 3.1 Flash TTS is the more cost-effective model for high-volume API pipelines.
Which model has a larger context window: Lyria 3 Pro or Gemini 3.1 Flash TTS?+
Lyria 3 Pro supports a context window of 131K tokens (~98k words), while Gemini 3.1 Flash TTS supports 8K tokens (~6k words). Therefore, Lyria 3 Pro can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Lyria 3 Pro or Gemini 3.1 Flash TTS?+
Lyria 3 Pro scores at the frontier of coding benchmarks, while Gemini 3.1 Flash TTS scores competitively for software generation. For complex autonomous debugging and agentic engineering, Lyria 3 Pro provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Lyria 3 Pro and Gemini 3.1 Flash TTS?+
Lyria 3 Pro is optimized for low-latency direct generation. In contrast, Gemini 3.1 Flash TTS focuses on standard fast inference.
How does inference speed (tokens per second) compare between Lyria 3 Pro and Gemini 3.1 Flash TTS?+
Lyria 3 Pro generates approximately 50 tokens per second, while Gemini 3.1 Flash TTS delivers 120 tokens per second. In latency-sensitive production environments, Gemini 3.1 Flash TTS provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Lyria 3 Pro and Gemini 3.1 Flash TTS to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 3.1 Flash TTS to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Lyria 3 Pro. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs